Reasoning on Grasp-Action Affordances

Paola Ardón, Èric Pairet, Ron Petrick, Subramanian Ramamoorthy, Katrin Lohan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Artificial intelligence is essential to succeed in challenging activities that involve dynamic environments, such as object manipulation tasks in indoor scenes. Most of the state-of-the-art literature explores robotic grasping methods by focusing exclusively on attributes of the target object. When it comes to human perceptual learning approaches, these physical qualities are not only inferred from the object, but also from the characteristics of the surroundings. This work proposes a method that includes environmental context to reason on an object affordance to then deduce its grasping regions. This affordance is reasoned using a ranked association of visual semantic attributes harvested in a knowledge base graph representation. The framework is assessed using standard learning evaluation metrics and the zero-shot affordance prediction scenario. The resulting grasping areas are compared with unseen labelled data to asses their accuracy matching percentage. The outcome of this evaluation suggest the autonomy capabilities of the proposed method for object interaction applications in indoor environments.
Original languageEnglish
Title of host publicationAnnual Conference Towards Autonomous Robotic Systems (TAROS)
EditorsK. Althoefer, J. Konstantinova, K. Zhang
PublisherSpringer, Cham
Chapter1
Pages3-15
Number of pages13
ISBN (Electronic)978-3-030-23807-0
DOIs
Publication statusPublished - 28 Jun 2019
Event20th Towards Autonomous Robotic Systems Conference - Graduate Centre of Mile End campus - Queen mary University of London (QMUL), London , United Kingdom
Duration: 3 Jul 20195 Jul 2019
Conference number: 20
https://www.qmul.ac.uk/robotics/events/taros2019/

Publication series

NameLecture Notes in Computer Science (LNCS) Towards Autonomous Robotic Systems
Volume11649
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th Towards Autonomous Robotic Systems Conference
Abbreviated titleTAROS 2019
Country/TerritoryUnited Kingdom
CityLondon
Period3/07/195/07/19
Internet address

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